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Author: Azad, Saeid KazemzadehSubject: Steel trusses

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    Article
    Citation - WoS: 11
    Citation - Scopus: 12
    A Standard Benchmarking Suite for Structural Optimization Algorithms: Iscso 2016-2022
    (Elsevier Science inc, 2023) Azad, Saeid Kazemzadeh; Azad, Saeıd Kazemzadeh; Azad, Sina Kazemzadeh; Azad, Saeıd Kazemzadeh; Kazemzadeh Azad, Saeid; Department of Civil Engineering; Department of Civil Engineering
    Benchmarking is an essential part of developing efficient structural optimization techniques. Despite the advent of numerous metaheuristic techniques for solving truss optimization problems, benchmarking new algorithms is often carried out using a selection of classic test examples which are indeed unchallenging for contemporary sophisticated optimization algorithms. Furthermore, the limited optimization results available in the literature on new test examples are usually not accurately comparable. This is typically due to the lack of infromation about the performance of the investigated algorithms and the inconsistencies between the studies in terms of adopted test examples for benchmarking, optimization problem formulation, maximum number of objective function evaluations and other similar issues. Accordingly, there exists a need for developing new standard test suites composed of easily reproducible challenging test examples with rigorous and comparable performance evaluation results of algorithms on these test suites. To this end, the present work aims to propose a new baseline for benchmarking structural optimization algorithms, using a set of challenging sizing and shape optimization problems of truss structures selected from the international student competition in structural optimization (ISCSO) instances. The most recent six structural optimization examples from the ISCSO are tackled using a representative metaheuristic structural optimization algorithm. The statistical results of all the optimization runs using the proposed benchmarking suite are provided to pave the way for more rigorous benchmarking of structural optimization algorithms.
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    Article
    Citation - WoS: 47
    Citation - Scopus: 46
    Enhanced Hybrid Metaheuristic Algorithms for Optimal Sizing of Steel Truss Structures With Numerous Discrete Variables
    (Springer, 2017) Azad, Saeid Kazemzadeh; Kazemzadeh Azad, Saeid
    The advent of modern computing technologies paved the way for development of numerous efficient structural design optimization tools in the recent decades. In the present study sizing optimization problem of steel truss structures having numerous discrete variables is tackled using combined forms of recently proposed metaheuristic techniques. Three guided, and three guided hybrid metaheuristic algorithms are developed by integrating a design oriented strategy to the stochastic search properties of three recently proposed metaheuristic optimization techniques, namely adaptive dimensional search, modified big bang-big crunch, and exponential big bang-big crunch algorithms. The performances of the proposed guided, and guided hybrid metaheuristic algorithms are compared to those of standard variants through optimum design of real-size steel truss structures with up to 728 design variables according to AISC-LRFD specification. The numerical results reveal that the hybrid form of adaptive dimensional search and exponential big bang-big crunch algorithm is the most promising algorithm amongst the other investigated techniques.